@@ -7,7 +7,7 @@ The source codes of this tutorial are in book/09.gan . Please refer to the instr
...
@@ -7,7 +7,7 @@ The source codes of this tutorial are in book/09.gan . Please refer to the instr
GAN \[[1](#References)\] is a kind of unsupervised learning method, which learns through games between two neural networks. This method was proposed by lan·Goodfellow et al in 2014, for whose paper you can refer to [Generative Adversarial Network](https://arxiv.org/abs/1406.2661)。
GAN \[[1](#References)\] is a kind of unsupervised learning method, which learns through games between two neural networks. This method was proposed by lan·Goodfellow et al in 2014, for whose paper you can refer to [Generative Adversarial Network](https://arxiv.org/abs/1406.2661)。
GAN is constituted by a generative network and a discrimination network. The generative network takes random sampling from latent space as input, while its output results need to imitate the real samples in training set to the greatest extent. The discrimination network takes real samples or the output of the generative network as input, aimed to distinguish the output of the generative network from real samples. And the generative network tries to cheat the discrimination network. These two networks confront each other and adjust parameters constantly in order to tell the samples generated by the generative network and the real samples apart. \[[2](#References)\] ）.
GAN is constituted by a generative network and a discrimination network. The generative network takes random sampling from latent space as input, while its output results need to imitate the real samples in training set to the greatest extent. The discrimination network takes real samples or the output of the generative network as input, aimed to distinguish the output of the generative network from real samples. And the generative network tries to cheat the discrimination network. These two networks confront each other and adjust parameters constantly in order to tell the samples generated by the generative network and the real samples apart. \[[2](#References)\] ）.
\[[2](#References)\] .
GAN is commonly used to generate convincing pictures that can \[[3](#References)\] ）. What's more, it can also generate videos and 3D object models etc.
GAN is commonly used to generate convincing pictures that can \[[3](#References)\] ）. What's more, it can also generate videos and 3D object models etc.
